The
The formula for a 2 word classifier is
In this instance, a positive result would be turned to 1 by the activation function, and a negative one to 0.
We are talking about a method to train the perceptron.
There are 2 cases:
We represent each word in the dictionary with a vector(of variable dimensions).
Another way to measure how similar two vectors are, but this time it's based on the angle and not the vectors length.
We build a neural network to solve the following problem:
The following is the architecture for skipgram, which has all the possible words in the first and last layer, each word has its own neuron:
We just make a sliding window on a text and we label as 1 the words that appear and 0 the words that don't appear.